Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Abstract

Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25-100%, and the kappa coefficients of the MLP range from 0.965-1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1-7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

“…Multilayer perceptron (MLP) is a commonly used neural network in remote sensing because of its relatively simple structure and higher classification capacity [36,69,70]. However, most traditional training methods usually fail to achieve proper parameters of MLP, i.e., weights and biases [38,51].…”

“…Currently, machine learning is recognized as the most promising technique for quantitative information retrieval from remotely sensed images [31]. A series of machine learning approaches were developed, such as maximum likelihood (ML) [32], support vector machines (SVMs) [10,33], random forest (RF) [34,35], neural networks (NNs) [36], and so on. Among the various machine learning methods, NN-based classifiers gain superiority in terms of robustness, high data error tolerance, and better classification performance [36][37][38].…”

“…A series of machine learning approaches were developed, such as maximum likelihood (ML) [32], support vector machines (SVMs) [10,33], random forest (RF) [34,35], neural networks (NNs) [36], and so on. Among the various machine learning methods, NN-based classifiers gain superiority in terms of robustness, high data error tolerance, and better classification performance [36][37][38]. When handling a complex dataset, multilayer perceptron (MLP) neural networks [39] are required, which feature more layers with a full connection between all neurons.…”

Multilayer Perceptron-Based Phenological and Radiometric Normalization for High-Resolution Satellite Imagery

“…Therefore, to balance the accuracy and time cost, three neurons for the hidden layer are selected in this experiment. As for the epochs and the learning rate, the figures 200 and 10 −4 are chosen, respectively, to prevent overfitting [34,51]. Furthermore, the activation function, loss function, and optimizer are selected as the rectified linear unit (ReLU), squared error, and adaptive moment estimation (ADAM), respectively.…”

“…Among all of the nonlinear regression models, the proposed method is based on multilayer perceptron (MLP), which has advantages over statistical methods, such as a lack of assumptions regarding the probabilistic models of data, robustness to noise, and the ability to learn complex and nonlinear patterns [29,30]. In particular, MLP has been verified as an excellent neural network algorithm at the pixel-level in remote sensing applications, such as cloud masking, image classification, water extraction, or change detection [31][32][33][34]. However, the application of MLP to radiometric and phenological normalizations of high-resolution satellite images is relatively rare.…”